Multi-dimensional signaling method for population-based metaheuristics: Solving the large-scale scheduling problem in smart grids

Soares, João; Ghazvini, Mohammad Ali Fotouhi; Silva, Marco; Vale, Zita

http://hdl.handle.net/10400.22/9393

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
ART_ZitaVale_GECAD_5_2016.pdf		4.84 MB	Adobe PDF	Download

Send Feedback

Authors

Soares, João

Ghazvini, Mohammad Ali Fotouhi

Silva, Marco

Vale, Zita

Abstract(s)

The dawn of smart grid is posing new challenges to grid operation. The introduction of Distributed Energy Resources (DER) requires tough planning and advanced tools to efﬁciently manage the system at reasonable costs. Virtual Power Players (VPP) are used as means of aggregating generation and demand, which enable smaller producers using different generation technologies to be more competitive. This paper discusses the problem of the centralized Energy Resource Management (ERM), including several types of resources, such as Demand Response (DR), Electric Vehicles (EV) and Energy Storage Systems (ESS) from the VPP's perspective to maximize proﬁts. The complete formulation of this problem, which includes the network constraints, is represented with a complex large-scale mixed integer nonlinear problem. This paper focuses on deterministic and metaheuristics methods and proposes a new multi-dimensional signaling approach for population-based random search techniques. The new approach is tested with two networks with high penetration of DERs. The results show outstanding performance with the proposed multi-dimensional signaling and conﬁrm that standard metaheuristics are prone to fail in solving these kind of problems.